Volume 15 , Issue 1 , PP: 62-76, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Himani Jain 1 , Amit Dixit 2 , Aditi Sharma 3 *
Doi: https://doi.org/10.54216/JCIM.150106
Image spam involves the practice of concealing text within an image. Various machine-learning techniques are used to categories image spam, utilizing a wide range of features extracted from the images. Convolutional neural networks (CNNs) are commonly used for image classification and feature extraction tasks because of their outstanding performance. In this study, our focus is to analyses image spam using a CNN model that incorporates deep learning techniques. This model has been meticulously fine-tuned and optimized to deliver exceptional performance in both feature extraction and classification tasks. In addition, we performed comparative evaluations of our model on different image spam datasets that were specifically created to make the classification task more challenging. The results we obtained show a significant improvement in classification accuracy compared to other methods used on the same datasets.
Deep Learning , Image Spam , Spam Detection , CNN , Social Sites
[1] N. M. AlShariah and A. K. Jilani Saudagar, “Detecting fake images on social media using machine learning,” Int. J. Adv. Comput. Sci. Appl., 2019, doi: 10.14569/ijacsa.2019.0101224.
[2] V. Sharma, I. You, K. Andersson, F. Palmieri, M. H. Rehmani, and J. Lim, “Security, privacy and trust for smart mobile-Internet of Things (M-IoT): A survey,” IEEE Access, 2020, doi: 10.1109/ACCESS.2020.3022661.
[3] S. Aphiwongsophon and P. Chongstitvatana, “Detecting fake news with machine learning method,” in ECTI-CON 2018 - 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2019. doi: 10.1109/ECTICon.2018.8620051.
[4] F. Aiwan and Y. Zhaofeng, “Image spam filtering using convolutional neural networks,” Pers. Ubiquitous Comput., 2018, doi: 10.1007/s00779-018-1168-8
[5] A. M, R. P, S. R, and K. S, “A secure model on Advanced Fake Image-Feature Network (AFIFN) based on deep learning for image forgery detection,” Pattern Recognit. Lett., 2021, doi: 10.1016/j.patrec.2021.10.011
[6] S. T. Suganthi et al., “Deep learning model for deep fake face recognition and detection,” PeerJ Comput. Sci., 2022, doi: 10.7717/PEERJ-CS.881.
[7] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, 2017, doi: 10.1145/3065386.
[8] G. Choudhary, V. Sharma, I. You, K. Yim, I. R. Chen, and J. H. Cho, “Intrusion Detection Systems for Networked Unmanned Aerial Vehicles: A Survey,” in 2018 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018, 2018. doi: 10.1109/IWCMC.2018.8450305.
[9] T. Sharmin, F. Di Troia, K. Potika, and M. Stamp, “Convolutional neural networks for image spam detection,” Inf. Secur. J., 2020, doi: 10.1080/19393555.2020.1722867.
[10] N. Imam and V. G. Vassilakis, “Detecting spam images with embedded Arabic text in twitter,” in 2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019, 2019. doi: 10.1109/ICDARW.2019.50107.
[11] M. Mateen, M. A. Iqbal, M. Aleem, and M. A. Islam, “A hybrid approach for spam detection for Twitter,” in Proceedings of 2017 14th International Bhurban Conference on Applied Sciences and Technology, IBCAST 2017, 2017. doi: 10.1109/IBCAST.2017.7868095.
[12] A. Annadatha and M. Stamp, “Image spam analysis and detection,” J. Comput. Virol. Hacking Tech., 2018, doi: 10.1007/s11416-016-0287-x.
[13] C. Fatichah, W. F. Lazuardi, D. A. Navastara, N. Suciati, and A. Munif, “Image spam detection on instagram using convolutional neural network,” in Lecture Notes in Networks and Systems, 2019. doi: 10.1007/978-981-13-6031-2_19.
[14] Bhatnagar, Amrita. , Giri, Arun. , Sharma, Aditi. A Hybrid Intrusion Detection Approach for Cyber Attacks. Journal of Journal of Cybersecurity and Information Management, vol. 13, no. 2, 2024, pp. 08-18. DOI: https://doi.org/10.54216/JCIM.130201
[15] B. Kim, S. Abuadbba, and H. Kim, “DeepCapture: Image Spam Detection Using Deep Learning and Data Augmentation,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020. doi: 10.1007/978-3-030-55304-3_24.
[16] A. Amir, B. Srinivasan, and A. I. Khan, “Distributed classification for image spam detection,” Multimed. Tools Appl., 2018, doi: 10.1007/s11042-017-4944-y.
[17] T. C. Lu, “CNN Convolutional layer optimisation based on quantum evolutionary algorithm,” Conn. Sci., 2021, doi: 10.1080/09540091.2020.1841111.
[18] D. H. Kim and H. Y. Lee, “Image manipulation detection using convolutional neural network,” Int. J. Appl. Eng. Res., 2017.
[19] A. Neisari, L. Rueda, and S. Saad, “Spam review detection using self-organizing maps and convolutional neural networks,” Comput. Secur., 2021, doi: 10.1016/j.cose.2021.102274.
[20] A. Choudhary, A. Tripathi, A. Sharma and R. Singh, "Evolution and comparative analysis of different Cloud Access Security Brokers in current era," 2022 International Conference on Fourth Industrial Revolution Based Technology and Practices (ICFIRTP), Uttarakhand, India, 2022, pp. 36-43, doi: 10.1109/ICFIRTP56122.2022.10059416.
[21] V. Sreenivasulu and M. A. Wajeed, “Image based classification of rumor information from the social network platform,” Trait. du Signal, 2021, doi: 10.18280/ts.380516.
[22] Ashish Dixit, R. P. Aggarwal, B. K. Sharma, Aditi Sharma. (2023). Safeguarding Digital Essence: A Subband DCT Neural Watermarking Paradigm Leveraging GRNN and CNN for Unyielding Image Protection and Identification. Journal of Journal of Intelligent Systems and Internet of Things, 10 ( 1 ), 33-47 (Doi : https://doi.org/10.54216/JISIoT.100103)
[23] V. Gupta, N. Kumar, A. Sharma and A. Abraham, "Sensor Routing Protocol with Optimized Delay and Overheads in Mobile based WSN", Journal of Information Assurance & Security, vol. 16, no. 4, 2021.
[24] K. V. Samarthrao and V. M. Rohokale, “Enhancement of email spam detection using improved deep learning algorithms for cyber security,” J. Comput. Secur., 2022, doi: 10.3233/JCS-200111.
[25] G. Stringhini et al., “Follow the green: Growth and dynamics in Twitter follower markets,” in Proceedings of the ACM SIGCOMM Internet Measurement Conference, IMC, 2013. doi: 10.1145/2504730.2504731.
[26] R. Asif and M. A. Islam, “Finding most collaborating mathematicians a co-Author network analysis of mathematics domain,” in 2016 International Conference on Computing, Electronic and Electrical Engineering, ICE Cube 2016 - Proceedings, 2016. doi: 10.1109/ICECUBE.2016.7495240.
[27] J. Hussain and M. A. Islam, “Evaluation of graph centrality measures for tweet classification,” in 2016 International Conference on Computing, Electronic and Electrical Engineering, ICE Cube 2016 - Proceedings, 2016. doi: 10.1109/ICECUBE.2016.7495209.
[28] Q. Li, Z. Qin, L. Chai, H. Zhang, J. Guo, and B. Bhanu, “Representative reference-set and betweenness centrality for scene image categorization,” in 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings, 2013. doi: 10.1109/ICIP.2013.6738670.
[29] Jiang Li, William Rich, Donald Buhl-Brown,"Texture Analysis of Remote Sensing Imagery with Clustering and Bayesian Inference", IJIGSP, vol.7, no.9, pp.1-10, 2015.DOI: 10.5815/ijigsp.2015.09.01
[30] Cariow, A.; Papli ́nski, J.P.;Makowska, M. VLSI-FriendlyFiltering Algorithms for Deep NeuralNetworks. Appl. Sci. 2023, 13, 9004.https://doi.org/10.3390/app13159004
